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Staff safety is not assured due to the indoor substation’s high environmental risk factor. The Chinese State Grid Corporation has been engaged in the intelligentization of substations and the employment of robots for inspection tasks. The autonomous navigation and positioning system of the mobile chassis is the most important feature of this type of robot, as it allows the robot to perceive the surrounding environment information at the initial position using its own sensors and find a suitable path to move to the target point to complete the task. Automatic navigation is the basis for the intelligentization of indoor substation robots, which is of great significance to the efficient and safe inspection of indoor substations. Based on this, this paper formulates a new navigation system, and builds a chassis simulation environment in the Robot Operating System (ROS). To begin with, we develop a novel hardware and sensor-based chassis navigation system experimental platform. Secondly, to conduct the fusion of the odometer and inertial navigation data, the Extended Kalman Filter (EKF) is used. The map’s creation approach determines how the environmental map is created. The global path is scheduled with the A* algorithm, whereas the local path is scheduled with the Dynamic Window Method (DWA). Finally, the created robot navigation system is applied to an auxiliary operation robot chassis suited for power distribution cabinet switch and the navigation system’s experimental analysis is conducted using this platform, demonstrating the system’s efficacy and practicability.

In the context of global ecological degradation and the gradual depletion of fossil energy sources, the importance of new energy power in production and development is rising year by year. In order to reduce the impact of fluctuations in wind power systems on the grid due to various reasons during grid connection, this paper proposes an improved Linear Active Disturbance Rejection Control (LADRC) combined with a neural network based grid-side inverter control method, which, based on the analysis of the characteristics of permanent magnet motors and the current situation of their use, enables the converter to show better control characteristics in terms of DC-side voltage fluctuation amplitude and recovery time during operation. Simulation results show that the controller is more robust and adaptable to the uncertainty and external disturbance changes of the converter under different operating conditions than the traditional control method. The system has good dynamic response performance.

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